Overview

Dataset statistics

Number of variables27
Number of observations355
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.0 KiB
Average record size in memory216.4 B

Variable types

Numeric21
Categorical6

Alerts

season has constant value "Pre-monsoon 2020" Constant
mandal has a high cardinality: 299 distinct values High cardinality
village has a high cardinality: 350 distinct values High cardinality
sno is highly correlated with district and 3 other fieldsHigh correlation
temp_id is highly correlated with sno and 2 other fieldsHigh correlation
pH is highly correlated with district and 5 other fieldsHigh correlation
E.C is highly correlated with TDS and 11 other fieldsHigh correlation
TDS is highly correlated with E.C and 11 other fieldsHigh correlation
CO3 is highly correlated with pHHigh correlation
HCO3 is highly correlated with pH and 7 other fieldsHigh correlation
Cl is highly correlated with E.C and 10 other fieldsHigh correlation
NO3 is highly correlated with E.C and 8 other fieldsHigh correlation
SO4 is highly correlated with E.C and 9 other fieldsHigh correlation
Na is highly correlated with E.C and 11 other fieldsHigh correlation
Ca is highly correlated with E.C and 6 other fieldsHigh correlation
Mg is highly correlated with E.C and 9 other fieldsHigh correlation
T.H is highly correlated with E.C and 9 other fieldsHigh correlation
SAR is highly correlated with E.C and 9 other fieldsHigh correlation
RSC meq / L is highly correlated with pH and 14 other fieldsHigh correlation
season is highly correlated with Classification and 2 other fieldsHigh correlation
Classification is highly correlated with pH and 13 other fieldsHigh correlation
district is highly correlated with sno and 6 other fieldsHigh correlation
Classification.1 is highly correlated with district and 3 other fieldsHigh correlation
long_gis is highly correlated with sno and 2 other fieldsHigh correlation
lat_gis is highly correlated with sno and 1 other fieldsHigh correlation
gwl is highly correlated with districtHigh correlation
F is highly correlated with pH and 6 other fieldsHigh correlation
mandal is uniformly distributed Uniform
village is uniformly distributed Uniform
sno has unique values Unique
temp_id has unique values Unique
CO3 has 292 (82.3%) zeros Zeros

Reproduction

Analysis started2023-08-10 11:46:19.437144
Analysis finished2023-08-10 11:53:17.223752
Duration6 minutes and 57.79 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

sno
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct355
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.3211268
Minimum1
Maximum379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:19.659186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.7
Q191.5
median193
Q3289.5
95-th percentile361.3
Maximum379
Range378
Interquartile range (IQR)198

Descriptive statistics

Standard deviation111.156852
Coefficient of variation (CV)0.5779752534
Kurtosis-1.231229045
Mean192.3211268
Median Absolute Deviation (MAD)99
Skewness-0.0362315332
Sum68274
Variance12355.84574
MonotonicityStrictly increasing
2023-08-10T17:23:19.939188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3791
 
0.3%
911
 
0.3%
1241
 
0.3%
1251
 
0.3%
1261
 
0.3%
1271
 
0.3%
1281
 
0.3%
1291
 
0.3%
1301
 
0.3%
1311
 
0.3%
Other values (345)345
97.2%
ValueCountFrequency (%)
11
0.3%
21
0.3%
31
0.3%
41
0.3%
51
0.3%
61
0.3%
71
0.3%
81
0.3%
91
0.3%
101
0.3%
ValueCountFrequency (%)
3791
0.3%
3781
0.3%
3771
0.3%
3761
0.3%
3751
0.3%
3741
0.3%
3731
0.3%
3721
0.3%
3711
0.3%
3701
0.3%

district
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
NALGONDA
33 
NIZAMABAD
 
23
KAMAREDDY
 
21
MEDAK
 
17
RANGAREDDY
 
17
Other values (28)
244 

Length

Max length17
Median length12
Mean length9.205633803
Min length5

Characters and Unicode

Total characters3268
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADILABAD
2nd rowADILABAD
3rd rowADILABAD
4th rowADILABAD
5th rowADILABAD

Common Values

ValueCountFrequency (%)
NALGONDA33
 
9.3%
NIZAMABAD23
 
6.5%
KAMAREDDY21
 
5.9%
MEDAK17
 
4.8%
RANGAREDDY17
 
4.8%
VIKARABAD16
 
4.5%
YADADRI15
 
4.2%
BHADRADRI14
 
3.9%
JAGITYAL14
 
3.9%
MAHABUBNAGAR12
 
3.4%
Other values (23)173
48.7%

Length

2023-08-10T17:23:21.180609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nalgonda33
 
8.7%
nizamabad23
 
6.1%
kamareddy21
 
5.5%
warangal18
 
4.7%
medak17
 
4.5%
rangareddy17
 
4.5%
vikarabad16
 
4.2%
yadadri15
 
4.0%
jagityal14
 
3.7%
bhadradri14
 
3.7%
Other values (25)191
50.4%

Most occurring characters

ValueCountFrequency (%)
A782
23.9%
D328
10.0%
R258
 
7.9%
N231
 
7.1%
L170
 
5.2%
M161
 
4.9%
G153
 
4.7%
I141
 
4.3%
B138
 
4.2%
E127
 
3.9%
Other values (16)779
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3175
97.2%
Space Separator35
 
1.1%
Open Punctuation29
 
0.9%
Close Punctuation29
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A782
24.6%
D328
10.3%
R258
 
8.1%
N231
 
7.3%
L170
 
5.4%
M161
 
5.1%
G153
 
4.8%
I141
 
4.4%
B138
 
4.3%
E127
 
4.0%
Other values (13)686
21.6%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
(29
100.0%
Close Punctuation
ValueCountFrequency (%)
)29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3175
97.2%
Common93
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A782
24.6%
D328
10.3%
R258
 
8.1%
N231
 
7.3%
L170
 
5.4%
M161
 
5.1%
G153
 
4.8%
I141
 
4.4%
B138
 
4.3%
E127
 
4.0%
Other values (13)686
21.6%
Common
ValueCountFrequency (%)
35
37.6%
(29
31.2%
)29
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A782
23.9%
D328
10.0%
R258
 
7.9%
N231
 
7.1%
L170
 
5.2%
M161
 
4.9%
G153
 
4.7%
I141
 
4.3%
B138
 
4.2%
E127
 
3.9%
Other values (16)779
23.8%

mandal
Categorical

HIGH CARDINALITY
UNIFORM

Distinct299
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Rajapet
 
4
Chandur
 
4
Anumula
 
4
Nakrekal
 
3
Nandipet
 
3
Other values (294)
337 

Length

Max length18
Median length16
Mean length8.861971831
Min length4

Characters and Unicode

Total characters3146
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)72.4%

Sample

1st rowAdilabad
2nd rowBazarhatnur
3rd rowGudihatnoor
4th rowJainath
5th rowNarnoor

Common Values

ValueCountFrequency (%)
Rajapet4
 
1.1%
Chandur4
 
1.1%
Anumula4
 
1.1%
Nakrekal3
 
0.8%
Nandipet3
 
0.8%
Nalgonda3
 
0.8%
Narayanpet3
 
0.8%
Dharoor3
 
0.8%
Qutubullapur3
 
0.8%
Shivampet3
 
0.8%
Other values (289)322
90.7%

Length

2023-08-10T17:23:21.540606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rajapet4
 
1.1%
chandur4
 
1.1%
anumula4
 
1.1%
dharoor3
 
0.8%
ghanpur3
 
0.8%
kunta3
 
0.8%
cc3
 
0.8%
qutubullapur3
 
0.8%
shivampet3
 
0.8%
narayanpet3
 
0.8%
Other values (294)334
91.0%

Most occurring characters

ValueCountFrequency (%)
a626
19.9%
l225
 
7.2%
r223
 
7.1%
u168
 
5.3%
n165
 
5.2%
d162
 
5.1%
e142
 
4.5%
p135
 
4.3%
i135
 
4.3%
h113
 
3.6%
Other values (44)1052
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2720
86.5%
Uppercase Letter394
 
12.5%
Space Separator17
 
0.5%
Other Punctuation7
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a626
23.0%
l225
 
8.3%
r223
 
8.2%
u168
 
6.2%
n165
 
6.1%
d162
 
6.0%
e142
 
5.2%
p135
 
5.0%
i135
 
5.0%
h113
 
4.2%
Other values (15)626
23.0%
Uppercase Letter
ValueCountFrequency (%)
K49
12.4%
M41
 
10.4%
N38
 
9.6%
A26
 
6.6%
B24
 
6.1%
T23
 
5.8%
C23
 
5.8%
D22
 
5.6%
G20
 
5.1%
P20
 
5.1%
Other values (14)108
27.4%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3114
99.0%
Common32
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a626
20.1%
l225
 
7.2%
r223
 
7.2%
u168
 
5.4%
n165
 
5.3%
d162
 
5.2%
e142
 
4.6%
p135
 
4.3%
i135
 
4.3%
h113
 
3.6%
Other values (39)1020
32.8%
Common
ValueCountFrequency (%)
17
53.1%
.7
21.9%
(3
 
9.4%
)3
 
9.4%
-2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a626
19.9%
l225
 
7.2%
r223
 
7.1%
u168
 
5.3%
n165
 
5.2%
d162
 
5.1%
e142
 
4.5%
p135
 
4.3%
i135
 
4.3%
h113
 
3.6%
Other values (44)1052
33.4%

village
Categorical

HIGH CARDINALITY
UNIFORM

Distinct350
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Angadipet
 
2
Dharoor
 
2
Kodur
 
2
Khanapur
 
2
Kanchanapalli
 
2
Other values (345)
345 

Length

Max length21
Median length16
Mean length9.38028169
Min length4

Characters and Unicode

Total characters3330
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique345 ?
Unique (%)97.2%

Sample

1st rowAdilabad
2nd rowBazarhatnur
3rd rowGudihatnoor
4th rowJainath
5th rowNarnoor

Common Values

ValueCountFrequency (%)
Angadipet2
 
0.6%
Dharoor2
 
0.6%
Kodur2
 
0.6%
Khanapur2
 
0.6%
Kanchanapalli2
 
0.6%
Papannapet1
 
0.3%
Yanampally1
 
0.3%
Maheswaram1
 
0.3%
Narketpalli1
 
0.3%
Narasapur1
 
0.3%
Other values (340)340
95.8%

Length

2023-08-10T17:23:21.869056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i3
 
0.8%
angadipet2
 
0.5%
nagar2
 
0.5%
b2
 
0.5%
tandur2
 
0.5%
s2
 
0.5%
somaram2
 
0.5%
22
 
0.5%
nagaram2
 
0.5%
12
 
0.5%
Other values (359)363
94.5%

Most occurring characters

ValueCountFrequency (%)
a683
20.5%
l272
 
8.2%
r230
 
6.9%
u175
 
5.3%
n162
 
4.9%
p159
 
4.8%
d142
 
4.3%
e140
 
4.2%
i137
 
4.1%
m128
 
3.8%
Other values (46)1102
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2870
86.2%
Uppercase Letter392
 
11.8%
Space Separator31
 
0.9%
Other Punctuation12
 
0.4%
Close Punctuation9
 
0.3%
Open Punctuation9
 
0.3%
Decimal Number7
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a683
23.8%
l272
 
9.5%
r230
 
8.0%
u175
 
6.1%
n162
 
5.6%
p159
 
5.5%
d142
 
4.9%
e140
 
4.9%
i137
 
4.8%
m128
 
4.5%
Other values (15)642
22.4%
Uppercase Letter
ValueCountFrequency (%)
K45
11.5%
M45
11.5%
B32
 
8.2%
N30
 
7.7%
A29
 
7.4%
S26
 
6.6%
R24
 
6.1%
P23
 
5.9%
D21
 
5.4%
G19
 
4.8%
Other values (14)98
25.0%
Decimal Number
ValueCountFrequency (%)
13
42.9%
23
42.9%
51
 
14.3%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
.12
100.0%
Close Punctuation
ValueCountFrequency (%)
)9
100.0%
Open Punctuation
ValueCountFrequency (%)
(9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3262
98.0%
Common68
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a683
20.9%
l272
 
8.3%
r230
 
7.1%
u175
 
5.4%
n162
 
5.0%
p159
 
4.9%
d142
 
4.4%
e140
 
4.3%
i137
 
4.2%
m128
 
3.9%
Other values (39)1034
31.7%
Common
ValueCountFrequency (%)
31
45.6%
.12
 
17.6%
)9
 
13.2%
(9
 
13.2%
13
 
4.4%
23
 
4.4%
51
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a683
20.5%
l272
 
8.2%
r230
 
6.9%
u175
 
5.3%
n162
 
4.9%
p159
 
4.8%
d142
 
4.3%
e140
 
4.2%
i137
 
4.1%
m128
 
3.8%
Other values (46)1102
33.1%

temp_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct355
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.867606
Minimum1001
Maximum4012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:22.173349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1055.5
Q11232.5
median1452
Q31670.5
95-th percentile1877.9
Maximum4012
Range3011
Interquartile range (IQR)438

Descriptive statistics

Standard deviation404.8700942
Coefficient of variation (CV)0.2686832557
Kurtosis8.834322484
Mean1506.867606
Median Absolute Deviation (MAD)220
Skewness2.415834993
Sum534938
Variance163919.7932
MonotonicityNot monotonic
2023-08-10T17:23:22.539063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10221
 
0.3%
11321
 
0.3%
11701
 
0.3%
11711
 
0.3%
11721
 
0.3%
11741
 
0.3%
11751
 
0.3%
16881
 
0.3%
11771
 
0.3%
11781
 
0.3%
Other values (345)345
97.2%
ValueCountFrequency (%)
10011
0.3%
10021
0.3%
10071
0.3%
10091
0.3%
10101
0.3%
10111
0.3%
10131
0.3%
10141
0.3%
10151
0.3%
10211
0.3%
ValueCountFrequency (%)
40121
0.3%
31371
0.3%
31171
0.3%
30881
0.3%
30411
0.3%
30401
0.3%
30361
0.3%
30351
0.3%
30341
0.3%
30291
0.3%

long_gis
Real number (ℝ≥0)

HIGH CORRELATION

Distinct353
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.74924306
Minimum77.444
Maximum80.900922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:22.932253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum77.444
5-th percentile77.65529
Q178.1485
median78.552568
Q379.290817
95-th percentile80.2030223
Maximum80.900922
Range3.456922
Interquartile range (IQR)1.142317

Descriptive statistics

Standard deviation0.7799561757
Coefficient of variation (CV)0.009904300605
Kurtosis-0.2937252792
Mean78.74924306
Median Absolute Deviation (MAD)0.528968
Skewness0.588555076
Sum27955.98129
Variance0.608331636
MonotonicityNot monotonic
2023-08-10T17:23:23.274928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.5082
 
0.6%
78.1142
 
0.6%
80.451
 
0.3%
78.4931
 
0.3%
79.0088881
 
0.3%
78.08991
 
0.3%
79.73241
 
0.3%
78.4051
 
0.3%
78.31941
 
0.3%
79.51531
 
0.3%
Other values (343)343
96.6%
ValueCountFrequency (%)
77.4441
0.3%
77.48121
0.3%
77.5000961
0.3%
77.50031
0.3%
77.5111
0.3%
77.5169561
0.3%
77.5261
0.3%
77.54151
0.3%
77.57571
0.3%
77.5911
0.3%
ValueCountFrequency (%)
80.9009221
0.3%
80.8261041
0.3%
80.8092411
0.3%
80.79111
0.3%
80.731
0.3%
80.6191
0.3%
80.61061
0.3%
80.581
0.3%
80.5681471
0.3%
80.5203181
0.3%

lat_gis
Real number (ℝ≥0)

HIGH CORRELATION

Distinct351
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.72078925
Minimum15.896441
Maximum19.730555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:23.695349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15.896441
5-th percentile16.3352381
Q117.121656
median17.6713
Q318.372871
95-th percentile19.1451079
Maximum19.730555
Range3.834114
Interquartile range (IQR)1.251215

Descriptive statistics

Standard deviation0.8663571857
Coefficient of variation (CV)0.04888931151
Kurtosis-0.7095312428
Mean17.72078925
Median Absolute Deviation (MAD)0.630491
Skewness0.1361186979
Sum6290.880182
Variance0.7505747732
MonotonicityNot monotonic
2023-08-10T17:23:24.135438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.542
 
0.6%
17.4452
 
0.6%
17.332
 
0.6%
17.472
 
0.6%
18.53341
 
0.3%
16.7165551
 
0.3%
17.1417521
 
0.3%
17.80811
 
0.3%
17.1640041
 
0.3%
17.7444661
 
0.3%
Other values (341)341
96.1%
ValueCountFrequency (%)
15.8964411
0.3%
15.941
0.3%
15.96561
0.3%
16.015181
0.3%
16.07011
0.3%
16.1071
0.3%
16.11561
0.3%
16.12871
0.3%
16.1296671
0.3%
16.1791
0.3%
ValueCountFrequency (%)
19.7305551
0.3%
19.68111
0.3%
19.66831
0.3%
19.63341
0.3%
19.5255551
0.3%
19.52191
0.3%
19.4956651
0.3%
19.4588881
0.3%
19.4441
0.3%
19.37891
0.3%

gwl
Real number (ℝ≥0)

HIGH CORRELATION

Distinct317
Distinct (%)89.8%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean11.98929178
Minimum1.4
Maximum49.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:24.682355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile3.412
Q16.2
median9.88
Q315.99
95-th percentile26.584
Maximum49.11
Range47.71
Interquartile range (IQR)9.79

Descriptive statistics

Standard deviation7.644774902
Coefficient of variation (CV)0.6376335683
Kurtosis1.792561066
Mean11.98929178
Median Absolute Deviation (MAD)4.58
Skewness1.223668934
Sum4232.22
Variance58.4425833
MonotonicityNot monotonic
2023-08-10T17:23:25.021986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.94
 
1.1%
16.53
 
0.8%
9.93
 
0.8%
4.53
 
0.8%
83
 
0.8%
18.13
 
0.8%
7.383
 
0.8%
9.432
 
0.6%
6.82
 
0.6%
12.672
 
0.6%
Other values (307)325
91.5%
ValueCountFrequency (%)
1.41
0.3%
1.551
0.3%
1.71
0.3%
1.761
0.3%
2.171
0.3%
2.221
0.3%
2.271
0.3%
2.431
0.3%
2.471
0.3%
2.571
0.3%
ValueCountFrequency (%)
49.111
0.3%
39.11
0.3%
37.81
0.3%
33.821
0.3%
332
0.6%
321
0.3%
31.431
0.3%
31.31
0.3%
31.21
0.3%
29.371
0.3%

season
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Pre-monsoon 2020
355 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters5680
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPre-monsoon 2020
2nd rowPre-monsoon 2020
3rd rowPre-monsoon 2020
4th rowPre-monsoon 2020
5th rowPre-monsoon 2020

Common Values

ValueCountFrequency (%)
Pre-monsoon 2020355
100.0%

Length

2023-08-10T17:23:25.376323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-08-10T17:23:25.812904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2020355
50.0%
pre-monsoon355
50.0%

Most occurring characters

ValueCountFrequency (%)
o1065
18.8%
0710
12.5%
2710
12.5%
n710
12.5%
355
 
6.2%
s355
 
6.2%
m355
 
6.2%
-355
 
6.2%
e355
 
6.2%
r355
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3195
56.2%
Decimal Number1420
25.0%
Space Separator355
 
6.2%
Dash Punctuation355
 
6.2%
Uppercase Letter355
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1065
33.3%
n710
22.2%
s355
 
11.1%
m355
 
11.1%
e355
 
11.1%
r355
 
11.1%
Decimal Number
ValueCountFrequency (%)
0710
50.0%
2710
50.0%
Space Separator
ValueCountFrequency (%)
355
100.0%
Dash Punctuation
ValueCountFrequency (%)
-355
100.0%
Uppercase Letter
ValueCountFrequency (%)
P355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3550
62.5%
Common2130
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1065
30.0%
n710
20.0%
s355
 
10.0%
m355
 
10.0%
e355
 
10.0%
r355
 
10.0%
P355
 
10.0%
Common
ValueCountFrequency (%)
0710
33.3%
2710
33.3%
355
16.7%
-355
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1065
18.8%
0710
12.5%
2710
12.5%
n710
12.5%
355
 
6.2%
s355
 
6.2%
m355
 
6.2%
-355
 
6.2%
e355
 
6.2%
r355
 
6.2%

pH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct137
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.008535211
Minimum7.02
Maximum9.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:26.041878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.02
5-th percentile7.4
Q17.76
median7.99
Q38.24
95-th percentile8.703
Maximum9.37
Range2.35
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.3814921577
Coefficient of variation (CV)0.04763569712
Kurtosis0.6526393318
Mean8.008535211
Median Absolute Deviation (MAD)0.24
Skewness0.4338681303
Sum2843.03
Variance0.1455362664
MonotonicityNot monotonic
2023-08-10T17:23:26.488560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9910
 
2.8%
7.948
 
2.3%
8.098
 
2.3%
8.288
 
2.3%
8.047
 
2.0%
7.817
 
2.0%
8.226
 
1.7%
8.236
 
1.7%
7.766
 
1.7%
7.666
 
1.7%
Other values (127)283
79.7%
ValueCountFrequency (%)
7.021
0.3%
7.171
0.3%
7.221
0.3%
7.262
0.6%
7.271
0.3%
7.292
0.6%
7.322
0.6%
7.331
0.3%
7.351
0.3%
7.381
0.3%
ValueCountFrequency (%)
9.371
0.3%
9.321
0.3%
9.171
0.3%
9.11
0.3%
9.081
0.3%
9.061
0.3%
8.871
0.3%
8.851
0.3%
8.841
0.3%
8.831
0.3%

E.C
Real number (ℝ≥0)

HIGH CORRELATION

Distinct322
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1268.96507
Minimum102
Maximum5175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:27.221958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile446.9
Q1768
median1085
Q31590
95-th percentile2550
Maximum5175
Range5073
Interquartile range (IQR)822

Descriptive statistics

Standard deviation746.1916204
Coefficient of variation (CV)0.588031647
Kurtosis5.59178293
Mean1268.96507
Median Absolute Deviation (MAD)368
Skewness1.93260619
Sum450482.6
Variance556801.9343
MonotonicityNot monotonic
2023-08-10T17:23:27.637815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7883
 
0.8%
10252
 
0.6%
9792
 
0.6%
7792
 
0.6%
13022
 
0.6%
10802
 
0.6%
8282
 
0.6%
14082
 
0.6%
14792
 
0.6%
6952
 
0.6%
Other values (312)334
94.1%
ValueCountFrequency (%)
1021
0.3%
1111
0.3%
2501
0.3%
3061
0.3%
3161
0.3%
3501
0.3%
3671
0.3%
3822
0.6%
3911
0.3%
4041
0.3%
ValueCountFrequency (%)
51751
0.3%
48781
0.3%
44291
0.3%
43771
0.3%
42641
0.3%
39611
0.3%
37171
0.3%
35531
0.3%
31891
0.3%
31711
0.3%

TDS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct322
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean812.1376451
Minimum65.28
Maximum3312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:27.945522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum65.28
5-th percentile286.016
Q1491.52
median694.4
Q31017.6
95-th percentile1632
Maximum3312
Range3246.72
Interquartile range (IQR)526.08

Descriptive statistics

Standard deviation477.562637
Coefficient of variation (CV)0.588031647
Kurtosis5.59178293
Mean812.1376451
Median Absolute Deviation (MAD)235.52
Skewness1.93260619
Sum288308.864
Variance228066.0723
MonotonicityNot monotonic
2023-08-10T17:23:28.272240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504.323
 
0.8%
491.522
 
0.6%
1017.62
 
0.6%
516.482
 
0.6%
403.22
 
0.6%
1687.042
 
0.6%
624.642
 
0.6%
946.562
 
0.6%
833.282
 
0.6%
901.122
 
0.6%
Other values (312)334
94.1%
ValueCountFrequency (%)
65.281
0.3%
71.041
0.3%
1601
0.3%
195.841
0.3%
202.241
0.3%
2241
0.3%
234.881
0.3%
244.482
0.6%
250.241
0.3%
258.561
0.3%
ValueCountFrequency (%)
33121
0.3%
3121.921
0.3%
2834.561
0.3%
2801.281
0.3%
2728.961
0.3%
2535.041
0.3%
2378.881
0.3%
2273.921
0.3%
2040.961
0.3%
2029.441
0.3%

CO3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.126760563
Minimum0
Maximum80
Zeros292
Zeros (%)82.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:28.575017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.82975177
Coefficient of variation (CV)2.502506526
Kurtosis9.210898281
Mean5.126760563
Median Absolute Deviation (MAD)0
Skewness2.90564428
Sum1820
Variance164.6025304
MonotonicityNot monotonic
2023-08-10T17:23:28.821300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0292
82.3%
2020
 
5.6%
3016
 
4.5%
1010
 
2.8%
408
 
2.3%
506
 
1.7%
802
 
0.6%
601
 
0.3%
ValueCountFrequency (%)
0292
82.3%
1010
 
2.8%
2020
 
5.6%
3016
 
4.5%
408
 
2.3%
506
 
1.7%
601
 
0.3%
802
 
0.6%
ValueCountFrequency (%)
802
 
0.6%
601
 
0.3%
506
 
1.7%
408
 
2.3%
3016
 
4.5%
2020
 
5.6%
1010
 
2.8%
0292
82.3%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.4366197
Minimum20
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:29.324155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile107
Q1190
median270
Q3350
95-th percentile490
Maximum950
Range930
Interquartile range (IQR)160

Descriptive statistics

Standard deviation123.4449868
Coefficient of variation (CV)0.4417638135
Kurtosis2.60966778
Mean279.4366197
Median Absolute Deviation (MAD)80
Skewness0.9673932529
Sum99200
Variance15238.66476
MonotonicityNot monotonic
2023-08-10T17:23:29.821663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29017
 
4.8%
18017
 
4.8%
30017
 
4.8%
23015
 
4.2%
26014
 
3.9%
22013
 
3.7%
33013
 
3.7%
13012
 
3.4%
35012
 
3.4%
36011
 
3.1%
Other values (46)214
60.3%
ValueCountFrequency (%)
201
 
0.3%
301
 
0.3%
701
 
0.3%
802
 
0.6%
905
1.4%
1008
2.3%
1104
 
1.1%
12011
3.1%
13012
3.4%
1404
 
1.1%
ValueCountFrequency (%)
9501
 
0.3%
8101
 
0.3%
6201
 
0.3%
5902
0.6%
5801
 
0.3%
5702
0.6%
5503
0.8%
5302
0.6%
5201
 
0.3%
5101
 
0.3%

Cl
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.0704225
Minimum10
Maximum1380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:30.158341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q170
median140
Q3250
95-th percentile530
Maximum1380
Range1370
Interquartile range (IQR)180

Descriptive statistics

Standard deviation185.593819
Coefficient of variation (CV)0.9612752516
Kurtosis8.85304929
Mean193.0704225
Median Absolute Deviation (MAD)80
Skewness2.51784931
Sum68540
Variance34445.06565
MonotonicityNot monotonic
2023-08-10T17:23:30.568465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6021
 
5.9%
7020
 
5.6%
5016
 
4.5%
3015
 
4.2%
4015
 
4.2%
8014
 
3.9%
12013
 
3.7%
11013
 
3.7%
9013
 
3.7%
22012
 
3.4%
Other values (55)203
57.2%
ValueCountFrequency (%)
102
 
0.6%
2011
3.1%
3015
4.2%
4015
4.2%
5016
4.5%
6021
5.9%
7020
5.6%
8014
3.9%
9013
3.7%
10010
2.8%
ValueCountFrequency (%)
13801
0.3%
10102
0.6%
9801
0.3%
9601
0.3%
9501
0.3%
8901
0.3%
8201
0.3%
7601
0.3%
7401
0.3%
6701
0.3%

F
Real number (ℝ≥0)

HIGH CORRELATION

Distinct189
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.106915493
Minimum0.04
Maximum4.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:31.106176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.28
Q10.6
median0.89
Q31.44
95-th percentile2.427
Maximum4.78
Range4.74
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation0.746359616
Coefficient of variation (CV)0.6742697349
Kurtosis3.568141594
Mean1.106915493
Median Absolute Deviation (MAD)0.35
Skewness1.608064789
Sum392.955
Variance0.5570526765
MonotonicityNot monotonic
2023-08-10T17:23:31.592292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.628
 
2.3%
0.456
 
1.7%
0.895
 
1.4%
0.725
 
1.4%
1.055
 
1.4%
0.595
 
1.4%
0.615
 
1.4%
0.835
 
1.4%
0.794
 
1.1%
0.564
 
1.1%
Other values (179)303
85.4%
ValueCountFrequency (%)
0.041
 
0.3%
0.1151
 
0.3%
0.141
 
0.3%
0.1471
 
0.3%
0.182
0.6%
0.191
 
0.3%
0.23
0.8%
0.211
 
0.3%
0.232
0.6%
0.2571
 
0.3%
ValueCountFrequency (%)
4.781
0.3%
4.571
0.3%
4.231
0.3%
3.641
0.3%
3.391
0.3%
3.261
0.3%
3.191
0.3%
3.171
0.3%
3.151
0.3%
3.111
0.3%

NO3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct321
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.0777023
Minimum0.026574
Maximum456.187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:31.902002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.026574
5-th percentile1.786497545
Q115.262334
median35.432
Q371.57264
95-th percentile211.66191
Maximum456.187
Range456.160426
Interquartile range (IQR)56.310306

Descriptive statistics

Standard deviation74.76762315
Coefficient of variation (CV)1.244515358
Kurtosis9.195300736
Mean60.0777023
Median Absolute Deviation (MAD)21.7021
Skewness2.700478631
Sum21327.58431
Variance5590.197472
MonotonicityNot monotonic
2023-08-10T17:23:32.518624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.4324
 
1.1%
13.72993
 
0.8%
19.664763
 
0.8%
0.8052727273
 
0.8%
44.088681822
 
0.6%
14.704282
 
0.6%
47.83322
 
0.6%
25.24532
 
0.6%
7.6500909092
 
0.6%
90.79452
 
0.6%
Other values (311)330
93.0%
ValueCountFrequency (%)
0.0265741
 
0.3%
0.0487191
 
0.3%
0.177161
 
0.3%
0.265741
 
0.3%
0.3144591
 
0.3%
0.354321
 
0.3%
0.4026363641
 
0.3%
0.8052727273
0.8%
1.062961
 
0.3%
1.107251
 
0.3%
ValueCountFrequency (%)
456.1871
0.3%
451.7581
0.3%
446.531781
0.3%
436.69941
0.3%
414.11151
0.3%
326.904491
0.3%
301.1721
0.3%
298.95751
0.3%
297.62881
0.3%
244.790831
0.3%

SO4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct145
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.67191549
Minimum3.75
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:33.018468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile8
Q113
median20
Q329.5
95-th percentile56.6
Maximum440
Range436.25
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation29.58777029
Coefficient of variation (CV)1.152534578
Kurtosis111.620464
Mean25.67191549
Median Absolute Deviation (MAD)7.75
Skewness8.79599765
Sum9113.53
Variance875.4361506
MonotonicityNot monotonic
2023-08-10T17:23:33.360049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1914
 
3.9%
2014
 
3.9%
1313
 
3.7%
2411
 
3.1%
2311
 
3.1%
1410
 
2.8%
1210
 
2.8%
1010
 
2.8%
229
 
2.5%
98
 
2.3%
Other values (135)245
69.0%
ValueCountFrequency (%)
3.751
 
0.3%
4.251
 
0.3%
4.51
 
0.3%
52
 
0.6%
5.951
 
0.3%
61
 
0.3%
75
1.4%
7.251
 
0.3%
7.451
 
0.3%
7.751
 
0.3%
ValueCountFrequency (%)
4401
0.3%
1801
0.3%
1581
0.3%
107.291
0.3%
1051
0.3%
98.51
0.3%
981
0.3%
961
0.3%
941
0.3%
861
0.3%

Na
Real number (ℝ≥0)

HIGH CORRELATION

Distinct258
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.808507
Minimum6.4
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:33.690058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile26.472
Q155.16
median88.18
Q3147.5
95-th percentile312.7
Maximum712
Range705.6
Interquartile range (IQR)92.34

Descriptive statistics

Standard deviation100.1166613
Coefficient of variation (CV)0.85710077
Kurtosis7.817124734
Mean116.808507
Median Absolute Deviation (MAD)37.44
Skewness2.407638212
Sum41467.02
Variance10023.34588
MonotonicityNot monotonic
2023-08-10T17:23:34.462045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316
 
1.7%
626
 
1.7%
725
 
1.4%
605
 
1.4%
295
 
1.4%
1155
 
1.4%
555
 
1.4%
614
 
1.1%
694
 
1.1%
1124
 
1.1%
Other values (248)306
86.2%
ValueCountFrequency (%)
6.41
0.3%
101
0.3%
122
0.6%
162
0.6%
171
0.3%
17.581
0.3%
191
0.3%
202
0.6%
20.91
0.3%
211
0.3%
ValueCountFrequency (%)
7121
0.3%
6231
0.3%
5351
0.3%
5321
0.3%
5151
0.3%
5111
0.3%
4861
0.3%
458.21
0.3%
402.61
0.3%
381.21
0.3%

K
Real number (ℝ≥0)

Distinct192
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.141746479
Minimum0.18
Maximum95.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:35.322926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile0.931
Q12
median3
Q35
95-th percentile20.615
Maximum95.8
Range95.62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.17139867
Coefficient of variation (CV)1.818928657
Kurtosis32.67070318
Mean6.141746479
Median Absolute Deviation (MAD)1.27
Skewness5.23879333
Sum2180.32
Variance124.8001484
MonotonicityNot monotonic
2023-08-10T17:23:36.825076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230
 
8.5%
323
 
6.5%
119
 
5.4%
419
 
5.4%
512
 
3.4%
66
 
1.7%
125
 
1.4%
2.54
 
1.1%
3.44
 
1.1%
3.94
 
1.1%
Other values (182)229
64.5%
ValueCountFrequency (%)
0.181
 
0.3%
0.21
 
0.3%
0.241
 
0.3%
0.341
 
0.3%
0.492
0.6%
0.562
0.6%
0.63
0.8%
0.621
 
0.3%
0.631
 
0.3%
0.682
0.6%
ValueCountFrequency (%)
95.81
0.3%
89.181
0.3%
891
0.3%
66.611
0.3%
63.31
0.3%
46.111
0.3%
44.41
0.3%
431
0.3%
37.51
0.3%
36.91
0.3%

Ca
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.21690141
Minimum8
Maximum488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:37.690143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile16
Q140
median64
Q388
95-th percentile176
Maximum488
Range480
Interquartile range (IQR)48

Descriptive statistics

Standard deviation53.44922874
Coefficient of variation (CV)0.7300121653
Kurtosis13.72567688
Mean73.21690141
Median Absolute Deviation (MAD)24
Skewness2.676409489
Sum25992
Variance2856.820053
MonotonicityNot monotonic
2023-08-10T17:23:38.743008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4044
12.4%
5632
 
9.0%
6430
 
8.5%
3226
 
7.3%
8026
 
7.3%
2425
 
7.0%
7224
 
6.8%
4820
 
5.6%
1618
 
5.1%
8818
 
5.1%
Other values (20)92
25.9%
ValueCountFrequency (%)
86
 
1.7%
1618
5.1%
2425
7.0%
3226
7.3%
4044
12.4%
4820
5.6%
5632
9.0%
6430
8.5%
7224
6.8%
8026
7.3%
ValueCountFrequency (%)
4881
 
0.3%
4001
 
0.3%
2641
 
0.3%
2161
 
0.3%
2083
0.8%
2003
0.8%
1923
0.8%
1844
1.1%
1765
1.4%
1682
 
0.6%

Mg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.77355493
Minimum4.862
Maximum233.376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:40.005058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.862
5-th percentile13.1274
Q124.31
median43.758
Q363.206
95-th percentile116.688
Maximum233.376
Range228.514
Interquartile range (IQR)38.896

Descriptive statistics

Standard deviation34.77078556
Coefficient of variation (CV)0.698579509
Kurtosis5.342466516
Mean49.77355493
Median Absolute Deviation (MAD)19.448
Skewness1.861501106
Sum17669.612
Variance1209.007529
MonotonicityNot monotonic
2023-08-10T17:23:40.933863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
38.89629
 
8.2%
24.3129
 
8.2%
19.44829
 
8.2%
34.03428
 
7.9%
48.6224
 
6.8%
29.17222
 
6.2%
14.58621
 
5.9%
43.75819
 
5.4%
53.48218
 
5.1%
63.20617
 
4.8%
Other values (26)119
33.5%
ValueCountFrequency (%)
4.8628
 
2.3%
9.72410
 
2.8%
14.58621
5.9%
19.44829
8.2%
24.3129
8.2%
29.17222
6.2%
34.03428
7.9%
38.89629
8.2%
401
 
0.3%
43.75819
5.4%
ValueCountFrequency (%)
233.3762
0.6%
194.481
 
0.3%
175.0321
 
0.3%
160.4461
 
0.3%
155.5843
0.8%
150.7221
 
0.3%
145.861
 
0.3%
140.9981
 
0.3%
131.2741
 
0.3%
126.4121
 
0.3%

T.H
Real number (ℝ≥0)

HIGH CORRELATION

Distinct223
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.492008
Minimum39.99177632
Maximum1939.703947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:41.894725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum39.99177632
5-th percentile139.9753289
Q1239.9424342
median339.9013158
Q3479.9136514
95-th percentile785.8610197
Maximum1939.703947
Range1899.712171
Interquartile range (IQR)239.9712172

Descriptive statistics

Standard deviation227.51835
Coefficient of variation (CV)0.5871562389
Kurtosis8.501700232
Mean387.492008
Median Absolute Deviation (MAD)119.9506579
Skewness2.163955716
Sum137559.6628
Variance51764.59959
MonotonicityNot monotonic
2023-08-10T17:23:42.616543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259.93421057
 
2.0%
179.96710536
 
1.7%
339.91776326
 
1.7%
219.95065795
 
1.4%
359.91776325
 
1.4%
379.90953954
 
1.1%
159.95888164
 
1.1%
99.975328954
 
1.1%
139.97532894
 
1.1%
319.95065794
 
1.1%
Other values (213)306
86.2%
ValueCountFrequency (%)
39.991776321
 
0.3%
59.991776321
 
0.3%
79.975328951
 
0.3%
99.975328954
1.1%
99.983552633
0.8%
119.96710533
0.8%
119.98355261
 
0.3%
139.95888161
 
0.3%
139.96710532
0.6%
139.97532894
1.1%
ValueCountFrequency (%)
1939.7039471
0.3%
1459.6710531
0.3%
1399.6052631
0.3%
1319.8684211
0.3%
1119.7368421
0.3%
1019.7286181
0.3%
1019.6052631
0.3%
979.77796051
0.3%
959.73684211
0.3%
899.83552631
0.3%

SAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct351
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.695397805
Minimum0.278283756
Maximum16.7072224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-10T17:23:43.375023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.278283756
5-th percentile0.6542491718
Q11.315678113
median2.051581275
Q33.11607108
95-th percentile7.247053391
Maximum16.7072224
Range16.42893864
Interquartile range (IQR)1.800392966

Descriptive statistics

Standard deviation2.314174552
Coefficient of variation (CV)0.8585651242
Kurtosis10.0563283
Mean2.695397805
Median Absolute Deviation (MAD)0.822644985
Skewness2.750662211
Sum956.8662209
Variance5.355403855
MonotonicityNot monotonic
2023-08-10T17:23:44.154764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2573570442
 
0.6%
1.1316860332
 
0.6%
1.4758528282
 
0.6%
2.0212733632
 
0.6%
2.2753666911
 
0.3%
1.635287711
 
0.3%
2.1611595761
 
0.3%
4.7443748381
 
0.3%
1.9342171921
 
0.3%
2.520531831
 
0.3%
Other values (341)341
96.1%
ValueCountFrequency (%)
0.2782837561
0.3%
0.3889163711
0.3%
0.3889169261
0.3%
0.397230361
0.3%
0.4125134061
0.3%
0.4812711391
0.3%
0.5500037371
0.3%
0.5523086471
0.3%
0.557010181
0.3%
0.5613404051
0.3%
ValueCountFrequency (%)
16.70722241
0.3%
15.710391051
0.3%
14.001727291
0.3%
13.887815321
0.3%
11.299782341
0.3%
10.569034061
0.3%
10.239411031
0.3%
9.9925441151
0.3%
9.8082719211
0.3%
9.5323336181
0.3%

Classification
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
C3S1
227 
C2S1
77 
C4S1
 
17
C3S2
 
14
C4S2
 
11
Other values (5)
 
9

Length

Max length4
Median length4
Mean length3.997183099
Min length3

Characters and Unicode

Total characters1419
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowC3S1
2nd rowC2S1
3rd rowC2S1
4th rowC3S1
5th rowC3S1

Common Values

ValueCountFrequency (%)
C3S1227
63.9%
C2S177
 
21.7%
C4S117
 
4.8%
C3S214
 
3.9%
C4S211
 
3.1%
C4S32
 
0.6%
C3S32
 
0.6%
C1S12
 
0.6%
C4S42
 
0.6%
O.G1
 
0.3%

Length

2023-08-10T17:23:44.989945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-08-10T17:23:46.174978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
c3s1227
63.9%
c2s177
 
21.7%
c4s117
 
4.8%
c3s214
 
3.9%
c4s211
 
3.1%
c4s42
 
0.6%
c1s12
 
0.6%
c3s32
 
0.6%
c4s32
 
0.6%
o.g1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S354
24.9%
C354
24.9%
1325
22.9%
3247
17.4%
2102
 
7.2%
434
 
2.4%
G1
 
0.1%
.1
 
0.1%
O1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter710
50.0%
Decimal Number708
49.9%
Other Punctuation1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S354
49.9%
C354
49.9%
G1
 
0.1%
O1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1325
45.9%
3247
34.9%
2102
 
14.4%
434
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin710
50.0%
Common709
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1325
45.8%
3247
34.8%
2102
 
14.4%
434
 
4.8%
.1
 
0.1%
Latin
ValueCountFrequency (%)
S354
49.9%
C354
49.9%
G1
 
0.1%
O1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S354
24.9%
C354
24.9%
1325
22.9%
3247
17.4%
2102
 
7.2%
434
 
2.4%
G1
 
0.1%
.1
 
0.1%
O1
 
0.1%

RSC meq / L
Real number (ℝ)

HIGH CORRELATION

Distinct295
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.061671145
Minimum-31.19407895
Maximum16.60082237
Zeros0
Zeros (%)0.0%
Negative240
Negative (%)67.6%
Memory size2.9 KiB
2023-08-10T17:23:46.924688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-31.19407895
5-th percentile-10.25799342
Q1-3.597368421
median-1.398190789
Q30.400411184
95-th percentile2.801891447
Maximum16.60082237
Range47.79490132
Interquartile range (IQR)3.997779605

Descriptive statistics

Standard deviation4.542889933
Coefficient of variation (CV)-2.203498819
Kurtosis7.116617572
Mean-2.061671145
Median Absolute Deviation (MAD)1.801480263
Skewness-1.380914626
Sum-731.8932566
Variance20.63784895
MonotonicityNot monotonic
2023-08-10T17:23:47.395044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6009868423
 
0.8%
-0.1980263163
 
0.8%
-0.9986842113
 
0.8%
-1.5983552633
 
0.8%
0.8008223683
 
0.8%
-1.3988486843
 
0.8%
0.2014802633
 
0.8%
-0.1990131583
 
0.8%
-1.7986842113
 
0.8%
-0.7978618422
 
0.6%
Other values (285)326
91.8%
ValueCountFrequency (%)
-31.194078951
0.3%
-20.793421051
0.3%
-19.397368421
0.3%
-18.192105261
0.3%
-16.994736841
0.3%
-15.596052631
0.3%
-15.594572371
0.3%
-13.795559211
0.3%
-12.3968751
0.3%
-12.395723681
0.3%
ValueCountFrequency (%)
16.600822371
0.3%
11.000986841
0.3%
9.8004934211
0.3%
8.8004934211
0.3%
8.2006578951
0.3%
7.4004934211
0.3%
7.0018092111
0.3%
5.8014802631
0.3%
5.6014802631
0.3%
5.6001644741
0.3%

Classification.1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
P.S.
313 
U.S.
 
23
MR
 
16
M.R
 
3

Length

Max length4
Median length4
Mean length3.901408451
Min length2

Characters and Unicode

Total characters1385
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP.S.
2nd rowMR
3rd rowP.S.
4th rowP.S.
5th rowP.S.

Common Values

ValueCountFrequency (%)
P.S.313
88.2%
U.S.23
 
6.5%
MR16
 
4.5%
M.R3
 
0.8%

Length

2023-08-10T17:23:48.310110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-08-10T17:23:49.356407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
p.s313
88.2%
u.s23
 
6.5%
mr16
 
4.5%
m.r3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
.675
48.7%
S336
24.3%
P313
22.6%
U23
 
1.7%
R19
 
1.4%
M19
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter710
51.3%
Other Punctuation675
48.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S336
47.3%
P313
44.1%
U23
 
3.2%
R19
 
2.7%
M19
 
2.7%
Other Punctuation
ValueCountFrequency (%)
.675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin710
51.3%
Common675
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S336
47.3%
P313
44.1%
U23
 
3.2%
R19
 
2.7%
M19
 
2.7%
Common
ValueCountFrequency (%)
.675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.675
48.7%
S336
24.3%
P313
22.6%
U23
 
1.7%
R19
 
1.4%
M19
 
1.4%

Interactions

2023-08-10T17:22:52.807481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:17:52.523174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:10.033425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:27.867303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:46.912042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:04.593584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:16.869990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:38.807018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:56.314420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:29.960599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:59.893497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:15.571973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:26.703082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:34.785569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:43.687836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:52.690498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:01.971862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:12.613438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:22.814605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:35.063664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:44.729709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:53.436310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:18:10.664571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:29.058092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:47.615010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:05.205884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:19:39.511018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:57.202425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:31.824815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:00.593683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:16.499233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:27.103087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:36.311632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:44.074834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:53.082495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:02.321861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:14.110348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:36.012351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:45.103379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:53.898237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:17:54.556272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:11.307491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:30.146836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:48.702183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:05.809669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:18.060313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:40.319010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:58.146405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:32.924599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:01.244441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:17.413005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:27.433095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:36.629426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:44.406834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:53.669501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:02.665857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:14.550336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:23.576139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:37.128832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:45.463082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:54.300751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:17:55.594097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:11.998993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:31.722022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:19:06.428000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:18.860922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:41.031017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:20:34.054983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:01.928420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:18.418178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:27.803092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:37.019433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:21:54.158499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:03.039855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:24.019568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:37.962274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:54.651657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:17:56.616316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:12.674506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:32.888208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:50.698231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:07.045407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:19.896319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:41.791026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:01.634427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:21:02.693608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:03.409862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:38.559548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:17:57.442548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:13.294679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:18:51.786337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:07.602023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:21.492251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:42.671016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:04.050401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:38.450062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2023-08-10T17:22:51.314213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:59.437429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:08.514559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:24.931679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:45.413206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:03.278239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:15.679689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:37.527016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:54.682995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:26.659931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:57.851388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:14.082132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:25.883083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:33.925560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:42.785832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:51.815538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:01.257860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:10.514865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:22.091003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:33.779454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:43.992099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:51.730783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:59.739121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:09.306476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:26.277610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:18:46.077716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:04.005713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:16.323477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:38.239017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:19:55.506420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:28.439428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:20:59.011813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:14.918822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:26.293091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:34.455574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:43.265836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:21:52.310498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:01.644866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:11.301865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:22.449720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:34.388050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:44.374745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-08-10T17:22:52.126035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2023-08-10T17:23:51.404795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-08-10T17:23:53.104808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-08-10T17:23:54.754640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-08-10T17:23:56.140088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-08-10T17:23:57.016377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-08-10T17:23:04.936888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-10T17:23:11.350371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-10T17:23:15.328507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

snodistrictmandalvillagetemp_idlong_gislat_gisgwlseasonpHE.CTDSCO3HCO3ClFNO3SO4NaKCaMgT.HSARClassificationRSC meq / LClassification.1
01ADILABADAdilabadAdilabad100178.52470019.66830014.15Pre-monsoon 20207.801671.01069.4404702300.5103.42240931.50154.013.007277.792499.8684212.994781C3S1-0.597368P.S.
12ADILABADBazarhatnurBazarhatnur100278.35083319.4588889.35Pre-monsoon 20207.95545.0348.800180801.0804.22768213.0094.012.001614.58699.9753294.087461C2S11.600493MR
23ADILABADGudihatnoorGudihatnoor100778.51222219.52555517.50Pre-monsoon 20208.12738.0472.320220300.45028.3858649.5022.01.004038.896259.9342110.593285C2S1-0.798684P.S.
34ADILABADJainathJainath100978.64000019.7305555.30Pre-monsoon 20208.051154.0738.5603001000.690140.92272714.7581.03.005668.068419.8848681.718668C3S1-2.397697P.S.
45ADILABADNarnoorNarnoor101078.85265419.4956654.50Pre-monsoon 20207.171042.0666.880370300.720163.06772710.2539.04.009663.206499.8930920.758400C3S1-2.597862P.S.
56ADILABADNeradigondaNeradigonda101178.41216019.2937509.90Pre-monsoon 20208.091063.0680.3203001000.56062.40863614.5065.014.006453.482379.9095391.449925C3S1-1.598191P.S.
67ADILABADTalamaduguTalamadugu101378.39690019.6334008.10Pre-monsoon 20207.94788.0504.320310400.44042.07550010.5059.04.004043.758279.9259871.533213C3S10.601480P.S.
78ADILABADTamsiTamsi101478.42680019.6811006.30Pre-monsoon 20208.32811.0519.04202001100.73011.47513616.00144.01.001614.58699.9753296.261642C3S12.400493MR
89ADILABADUtnoorUtnoor101578.76850019.3789006.55Pre-monsoon 20207.99422.0270.080160300.45013.4883188.2541.02.002419.448139.9671051.506756C2S10.400658P.S.
910BHADRADRIAnnapureddypalliAnnapureddypalli102180.82610417.35451917.30Pre-monsoon 20208.28250.0160.00090100.1475.3148006.006.41.77249.72499.9835530.278284C2S1-0.199671P.S.

Last rows

snodistrictmandalvillagetemp_idlong_gislat_gisgwlseasonpHE.CTDSCO3HCO3ClFNO3SO4NaKCaMgT.HSARClassificationRSC meq / LClassification.1
345370YADADRIRajapetBondugala187278.96650017.7683059.80Pre-monsoon 20208.281052.0673.2802601702.226.1120220.099.202.995643.758319.9259872.411343C3S1-1.198520P.S.
346371YADADRIRajapetKurraram187378.92986617.77549017.34Pre-monsoon 20207.271009.0645.7602401300.7565.1063023.055.003.3511224.310379.9588821.226780C3S1-2.799178P.S.
347372YADADRIRajapetRajapet187578.93296817.7294317.38Pre-monsoon 20207.221408.0901.1202303001.7036.9378619.076.473.7413648.620539.9177631.430868C3S1-6.198355P.S.
348373YADADRIRajapetSomaram187678.99178517.72567116.58Pre-monsoon 20207.66708.0453.120290402.3014.7042810.043.552.775634.034279.9424341.131686C2S10.201151P.S.
349374YADADRIRamannapetRamanapet187779.09509117.28312112.60Pre-monsoon 20207.673171.02029.4402708901.7235.4320030.0215.602.50192155.5841119.7368422.801321C4S1-16.994737P.S.
350375YADADRIS.NarayanpurS.Narayanpur188078.86001017.14471925.90Pre-monsoon 20207.402204.01410.5602903001.11436.6994032.0148.002.00144102.102779.8273032.304277C3S1-9.796546P.S.
351376YADADRIThurkapallyGandamalla188178.85383117.7331016.73Pre-monsoon 20207.881363.0872.3203801702.2335.6091625.0119.005.397258.344419.9013162.524908C3S1-0.798026P.S.
352377YADADRIValigondaT. somaram188378.95229017.39995313.62Pre-monsoon 20207.66708.0453.120290402.3014.7042810.043.552.775634.034279.9424341.131686C2S10.201151P.S.
353378YADADRIValigondaVemulakonda188579.14343317.3477824.07Pre-monsoon 20207.634878.03121.92035013800.8036.89357105.0532.007.3040077.7921319.8684216.366760C4S2-19.397368P.S.
354379YADADRIY.GuttaMallapuram188678.91163817.63355516.78Pre-monsoon 20207.461614.01032.9602503501.7744.9986422.094.003.7415253.482599.9095391.668619C3S1-6.998191P.S.